scholarly journals O3 High-dimensional analysis of tumor architecture predicts cancer immunotherapy response

2020 ◽  
Vol 8 (Suppl 2) ◽  
pp. A4.2-A5
Author(s):  
CM Schürch ◽  
DJ Phillips ◽  
M Matusiak ◽  
B Rivero Gutierrez ◽  
SS Bhate ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Cells ◽  
Immune Cells ◽  
Cell Types ◽  
Advisory Board ◽  
High Dimensional ◽  
Single Cell Level ◽  
Cell Type ◽  
Cell Level ◽  
Research Grant

BackgroundImmunotherapies have induced long-lasting remissions in countless advanced-stage cancer patients, but many more patients have not benefitted. Therefore, novel predictive markers are needed to stratify patients before treatment and select those who will most likely benefit from immunotherapy, while avoiding potentially devastating adverse effects and high treatment costs for those who will not. We reasoned that thoroughly characterizing the architecture of the tumor microenvironment (TME) at the single-cell level by highly multiplexed tissue imaging should reveal novel spatial biomarkers of immunotherapy response.Materials and MethodsWe used CODEX (CO-Detection by indEXing) highly multiplexed fluorescence microscopy to investigate the TME of cutaneous T cell lymphoma (CTCL) in samples from patients treated with pembrolizumab. 55 protein markers were visualized simultaneously using a tissue microarray of matched pre- and post-treatment skin biopsies from 7 pembrolizumab responders and 7 non-responders. After computational image processing and extraction of single-cell information, cell types were identified by unsupervised clustering followed by supervised curation, and cell-cell distances and ‘cellular neighborhoods’ were computed. We also performed RNA sequencing on laser-capture microdissected tissue microarray cores to extract cell-type specific gene expression profiles by CIBERSORTx analysis.ResultsCODEX enabled the identification and characterization of malignant CD4+ tumor cells and reactive immune cells in the CTCL TME at the single-cell level, resulting in 21 different cell type clusters with spatial information. Cluster frequencies were not significantly different between responders and non-responders pre- and post-treatment. However, advanced computational analysis of the tumor architecture revealed cellular neighborhoods (CNs) that dynamically changed during pembrolizumab therapy and were correlated with response. Effector-type CNs enriched in tumor-infiltrating CD4+ T cells and dendritic cells were significantly increased after treatment in responders. In contrast, a regulatory T cell-enriched CN was significantly increased in non-responders before and after therapy. Furthermore, a spatial signature of cell-cell distances between tumor cells and effector/regulatory immune cells predicted therapy outcome. In addition, CIBERSORTx analysis revealed that tumor cells in responders, but not in non-responders, increased their expression of immune-activating genes.ConclusionsHigh-dimensional spatial analysis of CTCL tumors revealed a pre-existing immunosuppressive state in pembrolizumab non-responders. Thorough analysis of the TME therefore enables the discovery of novel spatial biomarkers in a concept that accounts for both cell type information and higher-order tumor architecture. Combining highly multiplexed microscopy with CIBERSORTx allows for the discovery of novel, predictive spatial biomarkers of immunotherapy response and will pave the way for future studies that functionally address these cell types and their interactions.Disclosure InformationC.M. Schürch: F. Consultant/Advisory Board; Modest; Enable Medicine, LLC. D.J. Phillips: None. M. Matusiak: None. B. Rivero Gutierrez: None. S.S. Bhate: None. G.L. Barlow: None. M.S. Khodadoust: B. Research Grant (principal investigator, collaborator or consultant and pending grants as well as grants already received); Significant; Corvus Pharmaceuticals. R. West: None. Y.H. Kim: B. Research Grant (principal investigator, collaborator or consultant and pending grants as well as grants already received); Significant; Merck, Horizon, Soligenix, miRagen, Forty Seven Inc., Neumedicine, Trillium, Galderma, Elorac. D. Speakers Bureau/Honoraria (speakers bureau, symposia, and expert witness); Significant; Innate Pharma, Eisai, Kyowa Hakko Kirin, Takeda, Seattle Genetics, Medivir, Portola Pharmaceuticals, Corvus Pharmaceuticals. G.P. Nolan: E. Ownership Interest (stock, stock options, patent or other intellectual property); Significant; Akoya Biosciences. F. Consultant/Advisory Board; Significant; Akoya Biosciences.

Abstract 439: High Dimensional Single-Cell Immune Contexture of Human Atherosclerotic Plaques and Blood

2017 ◽  
Vol 37 (suppl_1) ◽  
Author(s):  
Adeeb Rahman ◽  
Aleksey Chudnovskiy ◽  
El-ad David Amir ◽  
Seunghee Kim-Schulze ◽  
Jennifer R Li ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
High Dimensional ◽  
Specific Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
Immune Contexture

Atherosclerosis is a disease characterized by immune infiltration of the arterial wall in response to tissue damage and systemic inflammation. In the era of precision medicine, is essential to gain insights on immune contexture of atherosclerotic tissue taking into account disease-specific cell variation in patients. We applied high-dimensional technologies for the analysis of multiple parameters at the single-cell level in clinical samples of patients undergoing carotid endatherectomy (CEA, n=15). Using time-of-flight mass-cytometry (CyTOF), we simultaneously analyzed 32 parameters at the single-cell level in peripheral blood mononuclear cells (PBMCs) and atherosclerotic-tissue associated immune cells of the same patient. Using viSNE, we mapped single-cell heterogeneity into two dimensions to discriminate PBMCs and tissue-associated CD45+ immune cells. Next, we employed Phenograph to cluster cells into phenotypically related populations, which were annotated based on canonical marker expression patterns. We identified several major immune subsets including two subsets of macrophages (CD163 low and CD163 high ), monocytes, dendritic cells (DCs), B and T cells. The most prevalent CD45+ cells identified in atherosclerotic tissue were CD4 + (25.8%) and CD8 + (25.2%) T cells, macrophages (12.8%), monocytes (7.7%) and B (2.1%) cells. Using a regression analysis similar to that employed by CITRUS, we determined that macrophages and a subset of CD8 T cells characterized by low expression of CD127 were selectively enriched in tissue vs. blood. Multiplexed immunohistochemistry confirmed that T cells comprised a major portion of the CD45+ cells in atherosclerotic tissue, even more abundant than macrophages. This study of deep phenotyping across-atherosclerotic tissue and blood demonstrate a significant T cell tissue infiltration of a specific subset of CD8 T cells. This suggests that adaptive T cell immunity plays a critical role in advanced atherosclerosis. The extension of this systems biology analysis pipeline to larger datasets can improve our understanding of the core mechanisms of chronic inflammation in atherosclerosis.

scholarly journals A rapid and robust method for single cell chromatin accessibility profiling

2018 ◽  
Author(s):  
Xi Chen ◽  
Ricardo J Miragaia ◽  
Kedar Nath Natarajan ◽  
Sarah A Teichmann
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Single Cell Level ◽  
Cell Type ◽  
Cell Level ◽  
Regulatory Regions ◽  
Cell Type Specific ◽  
Accessible Chromatin

AbstractThe assay for transposase-accessible chromatin using sequencing (ATAC-seq) is widely used to identify regulatory regions throughout the genome. However, very few studies have been performed at the single cell level (scATAC-seq) due to technical challenges. Here we developed a simple and robust plate-based scATAC-seq method, combining upfront bulk Tn5 tagging with single-nuclei sorting. We demonstrated that our method worked robustly across various systems, including fresh and cryopreserved cells from primary tissues. By profiling over 3,000 splenocytes, we identify distinct immune cell types and reveal cell type-specific regulatory regions and related transcription factors.

Critical downstream analysis steps for single-cell RNA sequencing data

2021 ◽  
Author(s):  
Zilong Zhang ◽  
Feifei Cui ◽  
Chen Lin ◽  
Lingling Zhao ◽  
Chunyu Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Noisy Data ◽  
Single Cell Level ◽  
Cell Type ◽  
Sequencing Data ◽  
Cell Level ◽  
Bioinformatics Tool ◽  
Single Cell Rna Sequencing ◽  
Downstream Analysis

Abstract Single-cell RNA sequencing (scRNA-seq) has enabled us to study biological questions at the single-cell level. Currently, many analysis tools are available to better utilize these relatively noisy data. In this review, we summarize the most widely used methods for critical downstream analysis steps (i.e. clustering, trajectory inference, cell-type annotation and integrating datasets). The advantages and limitations are comprehensively discussed, and we provide suggestions for choosing proper methods in different situations. We hope this paper will be useful for scRNA-seq data analysts and bioinformatics tool developers.

scholarly journals Single-cell transcriptomic analysis elucidates APOE genotype specific changes across cell types in two brain regions in Alzheimer’s disease

2021 ◽  
Author(s):  
Stella Belonwu ◽  
Yaqiao Li ◽  
Daniel Bunis ◽  
Arjun Arkal Rao ◽  
Caroline Warly Solsberg ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Single Cell ◽  
Molecular Mechanisms ◽  
Apoe Genotype ◽  
Cell Types ◽  
Brain Regions ◽  
Single Cell Level ◽  
Cell Level ◽  
Single Nucleus

Abstract Alzheimer’s Disease (AD) is a complex neurodegenerative disease that gravely affects patients and imposes an immense burden on caregivers. Apolipoprotein E4 (APOE4) has been identified as the most common genetic risk factor for AD, yet the molecular mechanisms connecting APOE4 to AD are not well understood. Past transcriptomic analyses in AD have revealed APOE genotype-specific transcriptomic differences; however, these differences have not been explored at a single-cell level. Here, we leverage the first two single-nucleus RNA sequencing AD datasets from human brain samples, including nearly 55,000 cells from the prefrontal and entorhinal cortices. We observed more global transcriptomic changes in APOE4 positive AD cells and identified differences across APOE genotypes primarily in glial cell types. Our findings highlight the differential transcriptomic perturbations of APOE isoforms at a single-cell level in AD pathogenesis and have implications for precision medicine development in the diagnosis and treatment of AD.

scholarly journals CyTOFmerge: integrating mass cytometry data across multiple panels

2019 ◽  
Vol 35 (20) ◽  
pp. 4063-4071 ◽  
Author(s):  
Tamim Abdelaal ◽  
Thomas Höllt ◽  
Vincent van Unen ◽  
Boudewijn P F Lelieveldt ◽  
Frits Koning ◽  
...  
Keyword(s):  
Single Cell ◽  
Biological Sample ◽  
Supplementary Information ◽  
High Dimensional ◽  
Single Cell Level ◽  
Supplementary Data ◽  
Mass Cytometry ◽  
Cell Level ◽  
Cellular Markers

Abstract Motivation High-dimensional mass cytometry (CyTOF) allows the simultaneous measurement of multiple cellular markers at single-cell level, providing a comprehensive view of cell compositions. However, the power of CyTOF to explore the full heterogeneity of a biological sample at the single-cell level is currently limited by the number of markers measured simultaneously on a single panel. Results To extend the number of markers per cell, we propose an in silico method to integrate CyTOF datasets measured using multiple panels that share a set of markers. Additionally, we present an approach to select the most informative markers from an existing CyTOF dataset to be used as a shared marker set between panels. We demonstrate the feasibility of our methods by evaluating the quality of clustering and neighborhood preservation of the integrated dataset, on two public CyTOF datasets. We illustrate that by computationally extending the number of markers we can further untangle the heterogeneity of mass cytometry data, including rare cell-population detection. Availability and implementation Implementation is available on GitHub (https://github.com/tabdelaal/CyTOFmerge). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Dissecting CLL through high-dimensional single-cell technologies

Blood ◽  
2019 ◽  
Vol 133 (13) ◽  
pp. 1446-1456
Author(s):  
Satyen H. Gohil ◽  
Catherine J. Wu
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Single Cell ◽  
Cancer Cells ◽  
Lymphocytic Leukemia ◽  
Response To Treatment ◽  
High Dimensional ◽  
Single Cell Level ◽  
Disease Course ◽  
Cell Level ◽  
New Techniques

Abstract We now have the potential to undertake detailed analysis of the inner workings of thousands of cancer cells, one cell at a time, through the emergence of a range of techniques that probe the genome, transcriptome, and proteome combined with the development of bioinformatics pipelines that enable their interpretation. This provides an unprecedented opportunity to better understand the heterogeneity of chronic lymphocytic leukemia and how mutations, activation states, and protein expression at the single-cell level have an impact on disease course, response to treatment, and outcomes. Herein, we review the emerging application of these new techniques to chronic lymphocytic leukemia and examine the insights already attained through this transformative technology.

scholarly journals Heterogeneity of PIK3CA mutational status at the single cell level in circulating tumor cells from metastatic breast cancer patients

2014 ◽  
Vol 9 (4) ◽  
pp. 749-757 ◽  
Author(s):  
Marta Pestrin ◽  
Francesca Salvianti ◽  
Francesca Galardi ◽  
Francesca De Luca ◽  
Natalie Turner ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Single Cell ◽  
Cancer Patients ◽  
Tumor Cells ◽  
Metastatic Breast ◽  
Single Cell Level ◽  
Breast Cancer Patients ◽  
Cell Level ◽  
Mutational Status

scholarly journals Combined Molecular Genetic and Cytogenetic Analysis from Single Cells after Isothermal Whole-Genome Amplification

2011 ◽  
Vol 57 (7) ◽  
pp. 1032-1041 ◽  
Author(s):  
Thomas Kroneis ◽  
Jochen B Geigl ◽  
Amin El-Heliebi ◽  
Martina Auer ◽  
Peter Ulz ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Cells ◽  
Whole Genome Amplification ◽  
Single Cells ◽  
Molecular Genetic ◽  
Target Cells ◽  
Whole Genome ◽  
Single Cell Level ◽  
Cell Level ◽  
Genome Amplification

BACKGROUND Analysis of chromosomal aberrations or single-gene disorders from rare fetal cells circulating in the blood of pregnant women requires verification of the cells' genomic identity. We have developed a method enabling multiple analyses at the single-cell level that combines verification of the genomic identity of microchimeric cells with molecular genetic and cytogenetic diagnosis. METHODS We used a model system of peripheral blood mononuclear cells spiked with a colon adenocarcinoma cell line and immunofluorescence staining for cytokeratin in combination with DNA staining with the nuclear dye TO-PRO-3 in a preliminary study to define candidate microchimeric (tumor) cells in Cytospin preparations. After laser microdissection, we performed low-volume on-chip isothermal whole-genome amplification (iWGA) of single and pooled cells. RESULTS DNA fingerprint analysis of iWGA aliquots permitted successful identification of all analyzed candidate microchimeric cell preparations (6 samples of pooled cells, 7 samples of single cells). Sequencing of 3 single-nucleotide polymorphisms was successful at the single-cell level for 20 of 32 allelic loci. Metaphase comparative genomic hybridization (mCGH) with iWGA products of single cells showed the gains and losses known to be present in the genomic DNA of the target cells. CONCLUSIONS This method may be instrumental in cell-based noninvasive prenatal diagnosis. Furthermore, the possibility to perform mCGH with amplified DNA from single cells offers a perspective for the analysis of nonmicrochimeric rare cells exhibiting genomic alterations, such as circulating tumor cells.

scholarly journals Single-cell transcriptome provides novel insights into antler stem cells, a cell type capable of mammalian organ regeneration

2018 ◽  
Author(s):  
Hengxing Ba ◽  
Datao Wang ◽  
Weiyao Wu ◽  
Hongmei Sun ◽  
Chunyi Li
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Single Cell ◽  
Large Cell ◽  
Stem Cell Markers ◽  
Single Cell Level ◽  
Cell Type ◽  
Cell Level ◽  
Homogeneous Population ◽  
Organ Regeneration

AbstractAntler regeneration, a stem cell-based epimorphic process, has potential as a valuable model for regenerative medicine. A pool of antler stem cells (ASCs) for antler development is located in the antlerogenic periosteum (AP). However, whether this ASC pool is homogenous or heterogeneous has not been fully evaluated. In this study, we produced a comprehensive transcriptome dataset at the single-cell level for the ASCs based on the 10x Genomics platform (scRNA-seq). A total of 4,565 ASCs were sequenced and classified into a large cell cluster, indicating that the ASCs resident in the AP are likely to be a homogeneous population. The scRNA-seq data revealed that tumor-related genes were highly expressed in these homogeneous ASCs: i.e. TIMP1, TMSB10, LGALS1, FTH1, VIM, LOC110126017 and S100A4. Results of screening for stem cell markers suggest that the ASCs may be considered as a special type of stem cell between embryonic (CD9) and adult (CD29, CD90, NPM1 and VIM) stem cells. Our results provide the first comprehensive transcriptome analysis at the single-cell level for the ASCs, and identified only one major cell type resident in the AP and some key stem cell genes, which may hold the key to why antlers, the unique mammalian organ, can fully regenerate once lost.

scholarly journals scAPAdb: a comprehensive database of alternative polyadenylation at single-cell resolution

2021 ◽  
Author(s):  
Sheng Zhu ◽  
Qiwei Lian ◽  
Wenbin Ye ◽  
Wei Qin ◽  
Zhe Wu ◽  
...  
Keyword(s):  
Single Cell ◽  
Alternative Polyadenylation ◽  
Cell Types ◽  
Single Cell Level ◽  
Cell Heterogeneity ◽  
Rna Seq ◽  
Cell Level ◽  
Eukaryotic Gene ◽  
User Friendly ◽  
Different Cell Types

Abstract Alternative polyadenylation (APA) is a widespread regulatory mechanism of transcript diversification in eukaryotes, which is increasingly recognized as an important layer for eukaryotic gene expression. Recent studies based on single-cell RNA-seq (scRNA-seq) have revealed cell-to-cell heterogeneity in APA usage and APA dynamics across different cell types in various tissues, biological processes and diseases. However, currently available APA databases were all collected from bulk 3′-seq and/or RNA-seq data, and no existing database has provided APA information at single-cell resolution. Here, we present a user-friendly database called scAPAdb (http://www.bmibig.cn/scAPAdb), which provides a comprehensive and manually curated atlas of poly(A) sites, APA events and poly(A) signals at the single-cell level. Currently, scAPAdb collects APA information from > 360 scRNA-seq experiments, covering six species including human, mouse and several other plant species. scAPAdb also provides batch download of data, and users can query the database through a variety of keywords such as gene identifier, gene function and accession number. scAPAdb would be a valuable and extendable resource for the study of cell-to-cell heterogeneity in APA isoform usages and APA-mediated gene regulation at the single-cell level under diverse cell types, tissues and species.

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